Knowledge-based ultrasound image enhancement

ABSTRACT

Image enhancement is provided for medical diagnostic ultrasound. Knowledge-based detection of anatomy or artifact identifies locations to be enhanced. The knowledge-based detection of the locations may avoid identification of other anatomy or artifacts. The image enhancement is applied to the identified locations and not others.

RELATED APPLICATION

This application is a continuation application of U.S. application Ser.No. 14/723,081, filed May 27, 2015, the entire contents of which arehereby incorporated by reference.

BACKGROUND

The present embodiments relate to medical diagnostic ultrasound imaging.In particular, ultrasound imaging is enhanced.

Traditional image enhancement algorithms are limited in their ability toperfect an ultrasound image. The image enhancement operates withoutspecific prior knowledge of the anatomy and pathology represented in theimage. Basic image processing techniques, such as low pass filters,reduce speckle noise but blur out anatomic structures.

In many cases, imaging artifacts have the same or similar properties toanatomic structures or tissue and hence are not detectable andeffectively segmentable by image processing algorithms. The imageprocessing may even enhance the artifacts. High pass filters used foredge enhancement enhance speckle noise. Techniques that are more complexrely on standard image analysis, such as gradient, variance, or simplyamplitude-based image segmentation to process selectively various partsof the image. While these techniques work on simpler images, images withartifacts (e.g., clutter, side lobes, grating lobes, or rib shadows) orother anatomy with similar properties may not respond as desired to thecomplex techniques. For example, gradient edge detection finds edges ofthe desired anatomy for filtering or enhancement, but may treat theartifact as an edge, enhancing a portion of the image not to beenhanced.

BRIEF SUMMARY

By way of introduction, the preferred embodiments described belowinclude methods, computer readable media, instructions, and systems forimage enhancement in medical diagnostic ultrasound. Knowledge-baseddetection of anatomy or artifact identifies locations to be enhanced(e.g., increased, suppressed, or processed in any other way). Theknowledge-based detection of the locations may avoid identification ofother anatomy or artifacts. The image enhancement is applied to theidentified locations and not others.

In a first aspect, a method is provided for image enhancement in medicaldiagnostic ultrasound. An ultrasound system acquires ultrasound imagedata from a scan of tissue of a patient. The ultrasound image datarepresents spatial locations of the tissue. A processor of theultrasound system applies a machine-learnt classifier to the ultrasoundimage data. The machine-learnt classifier outputs locations of animaging artifact of the ultrasound system in the ultrasound image data.The ultrasound image data is altered for the locations of the imagingartifact differently than for other locations. An ultrasound image ofthe tissue of the patient is displayed from the altered ultrasound imagedata.

In a second aspect, a non-transitory computer readable storage mediumhas stored therein data representing instructions executable by aprogrammed processor for image enhancement in medical diagnosticultrasound. The storage medium includes instructions for receiving, froman ultrasound scanner, detected ultrasound data representing a patient,classifying locations represented by the detected ultrasound data, theclassifying being with a knowledge base, enhancing the detectedultrasound data as a function of the classification of the locations,the enhancing changing amplitude of the ultrasound data for some of thelocations relative to other locations while maintaining representationof all of the locations, and generating an image from the enhancedultrasound data.

In a third aspect, a system is provided for image enhancement in medicaldiagnostic ultrasound. A receive beamformer is configured to acquireultrasound data representing a region of a patient. A B-mode detector,Doppler estimator, or both are configured to output detected data fromthe ultrasound data. A processor is configured to extract input featuresfrom the detected data, identify an artifact from the detected data as afunction of a knowledge base, and image process the detected data as afunction of the artifact. A display is configured to display an image ofthe region based on the image processed detected data.

Further aspects and advantages of the invention are discussed below inconjunction with the preferred embodiments. The present invention isdefined by the following claims, and nothing in this section should betaken as a limitation on those claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The components and the figures are not necessarily to scale, emphasisinstead being placed upon illustrating the principles of the invention.Moreover, in the Figures, like reference numerals designatecorresponding parts throughout the different views.

FIG. 1 is a flow chart diagram of one embodiment of a method for imageenhancement in medical diagnostic ultrasound;

FIG. 2 is an example image of a heart with a grating lobe artifact;

FIG. 3 is an example of the image of the heart of FIG. 2 enhanced toremove or reduce the grating lobe through image processing;

FIG. 4 is an example image of the heart with spontaneous contrast fromblood;

FIG. 5 is an example image of the heart of FIG. 4 enhanced to remove orreduce the spontaneous contrast from blood; and

FIG. 6 is a block diagram of one embodiment of a system for imageenhancement in medical diagnostic ultrasound.

DETAILED DESCRIPTION OF THE DRAWINGS AND PRESENTLY PREFERRED EMBODIMENTS

Knowledge-based enhancement of ultrasound images is provided.Knowledge-based feature detection techniques may successfully detectanatomic structures or artifacts in an ultrasound image withoutdetecting other objects. These detection techniques are harnessed toimprove and make smarter image processing or enhancement algorithms.Post-acquisition image enhancement benefits from application ofknowledge-based detection. Already acquired image data may be altered byimage processing localized specifically to detected anatomy orartifacts.

In one embodiment, imaging artifacts are identified for enhancedimaging. The knowledge is captured as expert user annotations ofartifacts in a knowledge database. This knowledge significantly improvesartifact detection and minimization. Utilizing knowledge-based detectionalgorithms to detect artifacts, such as grating lobes, side lobes fromrib reflections, rib shadows, other shadows, or spontaneous contrast,provides focused image enhancement. The output of the knowledge-basedalgorithms is used for improved image processing to remove a capturedartifact.

FIG. 1 shows one embodiment of a method of image enhancement in medicaldiagnostic ultrasound. This embodiment is directed to detecting anatomyor artifacts using knowledge-based detection. A collection of expertannotated examples is used to identify more accurately locations ofanatomy or artifacts. Imaging from the already acquired data is enhancedby processing differently for the identified locations than for otherlocations. Image enhancement is improved by the detection provided witha knowledge base.

Additional, different, or fewer acts may be provided. For example, acts16 and 18 are not provided. As another example, acts for activatingand/or configuring the ultrasound scanning are provided. The acts aredirected to application of the knowledge base. Acts for creating andlearning from the knowledge base may be provided in alternative oradditional embodiments. The acts are performed in the order shown or adifferent order.

In act 12, an ultrasound system acquires ultrasound data from a scan oftissue of a patient. The ultrasound data represents the patient. Amedical diagnostic ultrasound system applies electrical signals to atransducer, which then converts the electrical energy to acoustic energyfor scanning a region of the patient. Echoes are received and convertedinto electrical signals by elements of the transducer for receiveoperation of the scan. Any type of scan, scan format, or imaging modemay be used. For example, harmonic imaging is used with or without addedcontrast agents. As another example, B-mode, color flow mode, spectralDoppler mode, M-mode, or other imaging mode is used.

Ultrasound data representing anatomical information is acquired from apatient. The ultrasound data represents a point, a line, an area, or avolume of the patient. Waveforms at ultrasound frequencies aretransmitted, and echoes are received. The acoustic echoes are convertedinto electrical signals and beamformed to represent sampled locationswithin a region of the patient.

The beamformed data may be filtered or otherwise processed. In oneembodiment, the ultrasound data is B-mode data representing tissuestructures. The beamformed data is detected, such as determining anintensity (e.g., B-mode or backscatter power or intensity). In anotherembodiment, the ultrasound data is flow or motion data for locationsassociated with a vessel, flow, or tissue motion. A sequence of echosignals from a same location may be used to estimate velocity, variance,and/or energy as the detection. Echoes at one or more harmonics of thetransmitted waveforms may be processed. The detected values may befiltered and/or scan converted to a display format. The ultrasound datarepresenting the patient is post detection data, such as detected databefore spatial and/or temporal filtering, filtered data before scanconversion, scan converted data before display mapping, or displaymapped image data before or after output to a display.

The ultrasound imaging system is a scanner that provides the detectedultrasound data representing the patient. The ultrasound system receivesthe detected ultrasound data, such as receiving B-mode data, as anoutput from the detector. A processor of the scanner or a remoteprocessor not part of the scanner receives the detected ultrasound datafor knowledge-based detection.

In act 14, a knowledge base is used to classify locations in thereceived ultrasound data. The classifier is applied to each locationrepresented by the data or is applied to distinguish between differentlocations. A processor classifies different locations represented by theultrasound data as belonging to a class or not. Other classifiers thanbinary classifiers may be used, such as classifying each location asbeing a member of one of three or more classes (e.g., (1) background,artifact, and anatomy; (2) fluid, bone, and tissue; or (3) organ ofinterest, other organ, and non-determinative). Different locationsbelong to different classes. Some locations may belong to the sameclass. The classifier detects which locations are members of a class,such as detection of the locations of particular anatomy or of aparticular artifact.

The processor classifies with a knowledge base. The knowledge baseincludes ground-truth labeled sets of ultrasound data. An expertannotates ultrasound images as including or not including the class andthe locations that include the class. For example, tens, hundreds, orthousands of B-mode images with an artifact are annotated to indicatethe location of the artifact. Another set of images without the artifactmay be provided. As another example, a database of images is labeled forone or more anatomical structures. The database of images is created byan expert user analyzing images and annotating the images for thecorresponding structures, features, and/or artifacts.

To classify with the knowledge base, one or more database images areidentified and used to detect the structure in a current image as partof classification. The identified database images are also used todetermine the shape or locations of the anatomical structure ofinterest.

The processor uses the knowledge base in classification by finding animage from the knowledge base most or sufficiently similar to theultrasound data received in act 12. Cross-correlation, minimum sum ofabsolute differences, other correlation, or other similarity measure maybe used. Scaling and/or spatial transformation may be applied to relatelocations from the current frame of ultrasound data to the images of theknowledge base. The resulting knowledge base image is annotated to showthe locations for the anatomy or artifact. The current frame ofultrasound data is labeled accordingly (i.e., corresponding locationsidentified as the anatomy or artifact). Other knowledge-basedclassification may be used.

In another embodiment, the processor uses the knowledge base by applyinga machine-learnt classifier. The machine-learnt classifier is learntfrom the database of annotated images. The annotated or ground-truthlabeled images are used as training data. A processor learns theclassification based on the ground truth and features extracted from theimages of the knowledge base. Through one or more variousmachine-learning processes, the classifier is trained to detectlocations of the anatomy and/or artifact.

Any features may be used for training and application. Haar wavelets,steerable features, directional features, machine-learnt features,and/or pattern matches are extracted from the image data for eachlocation. A kernel of any size and/or shape is centered or positionedbased on the location for which the feature is being extracted. Thefeature extraction occurs for each of the locations.

Any machine-learning algorithm or approach to classification may beused. For example, a support vector machine (e.g., 2-norm SVM), linearregression, boosting network, probabilistic boosting tree, lineardiscriminant analysis, relevance vector machine, neural network,Bayesian network, combinations thereof, or other now known or laterdeveloped machine learning is provided. Binary, hierarchal, or otherlearning processes may be used. Supervised or semi-supervised trainingmay be used.

The machine learning provides a matrix or other output. The matrix isderived from analysis of the database of training data with knownresults. The machine-learning algorithm determines the relationship ofdifferent inputs to the result. The learning may select only a sub-setof input features or may use all available input features. A programmermay influence or control which input features to use or otherperformance of the training. For example, the programmer may limit theavailable features to information available in a given type ofultrasound imaging. The matrix associates input features with outcomes,providing a model for classifying. Machine training providesrelationships using one or more input variables with outcome, allowingfor verification or creation of interrelationships not easily performedmanually.

The model represents a probability of a location represented byultrasound data being of the class or not. This probability is alikelihood of membership in the class. A range of probabilitiesassociated with different possible states (e.g., binary or a three ormore possible states) is output. Alternatively, the class of thelocation is assigned based on the highest probability. In otherembodiments, the class per location is output without probabilityinformation.

For application of the classifier, a same or different processorextracts discriminative input features from the received ultrasound dataor data derived from the ultrasound data (e.g., filtered orparameterized data). An input feature vector of information is input tothe classifier. The values of the input features are applied to themachine-learnt classier. The processor of the ultrasound system or otherprocessor applies the classifier to the received ultrasound data todetermine locations of the anatomy and/or artifact. The processorapplies a matrix or other classifier construction to output the class ofeach of a plurality of locations represented by the ultrasound data.Locations of an anatomic structure or locations of an artifact areoutput based on the knowledge base incorporated into the machine-learntclassifier.

In one embodiment, an artifact is detected. Any artifact may bedetected. The artifact is caused by the ultrasound system scanning, suchas being due to a transducer characteristic, a scan format, scanningparameters, or image processing. Alternatively, the artifact is causedby anatomy, such as due to aberration or reflection. The artifactpresents as a false object, shadowing, or other information notrepresentative of actual anatomy at the location or representative ofanatomy not to be detected.

For example, the ultrasound data represents one or more grating lobes.The classifier detects the location of any grating lobes. Using aknowledge base of images with and without grating lobes for a sameapplication (e.g., heart imaging) and/or configuration (e.g.,transducer, frequency, scan format, or other), grating lobe artifactsare detected in the ultrasound data. FIG. 2 shows an example image of aheart. A grating lobe causes a tissue representation in a fluid region.The area delineated at 26 shows the grating lobe. An image segmentationalgorithm may identify the grating lobe as tissue instead of anartifact. To avoid aggressive enhancement approaches making alterationof the wrong data, the classifier detects the locations with intensitycaused by the grating lobe as distinguished from other objects.

As another example, the ultrasound data represents one or more regionsof spontaneous contrast of blood cells. The spontaneous contrast isB-mode response to blood, such as blood in a heart chamber. Traditionalultrasound systems do not detect response from blood for B-mode imaging.Modern ultrasound machines are sensitive enough to pick up signals fromblood circulating in the heart chamber. While this speaks to the highquality of ultrasound acquisition, the signal from blood may bedistracting to the user trying to assess anatomy in a B-mode image.These spontaneous contrast characteristics of signal from blood areclose enough to that from tissue, that image enhancements may treat theblood like tissue. FIG. 4 shows an example four-chamber view of theheart. The spontaneous contrast occurs primarily in two of the chambers.The areas delineated as 28 show the spontaneous contrast. Thespontaneous contrast in the left ventricle (upper chamber) may beparticularly distracting. The classifier detects the locations withintensity caused by the spontaneous contrast. The detection may be ofall locations (e.g., both chambers noted in FIG. 4) or less than alllocations (e.g., just the spontaneous contrast in the left ventricle).

In other examples, the ultrasound data represents shadowing. A region ofdense tissue or bone may at least partially block ultrasound scanningfrom part of or the entire array, causing shadowing. In yet anotherexample, bone causes lobe like artifacts due to reflecting orredirecting ultrasound data (e.g., acting as a mirror for acousticenergy). The classifier detects one or more of these artifacts. Otherartifacts may be detected by the classifier.

In additional or alternative embodiments, one or more anatomicalstructures are detected by the classifier. The classifier is trained todetect any anatomical structure or part of anatomy, such as detectingthe liver, heart, valve, left ventricle, or other anatomy. More than oneanatomical structure may be located. A collection of features or asingle feature occurring with respect to no specific anatomy or withrespect to different anatomy may be detected.

Different classifiers are trained for different artifacts and/oranatomy. The same or different classifiers may be trained for differentimaging situations, such a classifier for detecting a grating lobeartifact for imaging the heart and a different classifier for detectinga grating lobe artifact for imaging the liver. Configuration specificclassifiers may be trained, such as one classifier for use with onetransducer and corresponding frequency and another classifier for usewith a different transducer and corresponding frequency. The same ordifferent classifiers may be trained to detect different objects, suchas one classifier for detecting an artifact and another classifier fordetecting anatomy.

Referring again to FIG. 1, the processor outputs an indication of thelocations in act 16. An image is generated from the ultrasound data. Thedetected locations for a given anatomy or artifact are indicated in theimage, such as by color, line graphic, probability map, or intensity.The image enhancement of act 20 is to be performed after receiving userconfirmation in act 18 of the accuracy of the detection of act 14. Theultrasound system processes the image after revealing the output of themachine-learnt classifier. The knowledge-based image processing is madetransparent to the user. The user is informed what is to be altered andwhy before the processor automatically alters large parts of an image.The user may edit the detection, such as changing the classification ofone or more locations. The confirmation by the user in act 18 is a keypress or other solicited confirmation. The confirmation may be optional.

In act 20, the processor, filter, ultrasound system, or combinationsthereof enhances the detected ultrasound data as a function of theclassification of the locations. In segmentation, detected data isremoved or isolated. For image enhancement, the background, othertissue, fluid, other object, or other representation by the ultrasounddata remains. Instead, the ultrasound data is altered to make somelocations more visible relative to other locations, to fill in gaps, toenlarge, to reduce, to separate, and/or otherwise image process thealready detected data. The image processing for some locations isdifferent than imaging processing for other locations. The enhancementchanges the amplitude of the ultrasound data for some locations more orless relative to other locations while maintaining representation of allof the locations. Some locations may be represented by zero orbackground values due to the alteration, but are still representedlocations.

Rather than applying image-processing algorithms relying on statisticaltools to find locations that may be anatomy, the knowledge base is usedto identify the locations. For example, locating gradients as anindication of an edge for filtering differently along the edge relies onstatistics that certain gradients are edges. Artifacts or other anatomymay have similar gradients, so improperly enhanced. Using the knowledgebase detection identifies the locations to which the different imageprocessing (e.g., low pass filtering along and high pass filteringperpendicular to an edge) is applied. Edges of artifacts or otheranatomy are not enhanced as much, in the same way, or at all. Thesedetected locations are used in image processing for smarter and betterimage enhancement. Similarly, image processing to reduce or removeartifacts is applied just to artifact locations rather than alllocations with similar statistical properties as the artifact.

Any image enhancement may be applied to the ultrasound data. Forexample, spatially adaptive filtering is applied. One or morecharacteristics of the filter adapt to the classification of thelocations. The spatial filter kernel (e.g., size and/or weights) or typeof filtering varies depending on the classification of the locationbeing filtered. Anatomy or a border of anatomy may be enhanced for moreor less filtering as compared to other locations. Edge detection,spatial filtering, temporal filtering, transformation, or other imageprocess may vary as a function of location of the anatomy and/orartifact identified by the classifier.

In one embodiment, the enhancement is through removal or reduction of anartifact. For example, a high pass filter or amplitude scaling (e.g.,reduction by an amount or %) is applied to locations associated with anartifact and not applied to or applied differently to other locationsrepresented by the ultrasound data.

In a grating lobe example, the grating lobe information is suppressed.FIG. 2 shows a grating lobe generally indicated at 26 in the leftventricle. The grating lobe may be distracting to the user. After highpass filtering and/or amplitude scaling, the intensities caused by thegrating lobe are reduced as reflected in FIG. 3. The aesthetics and/ordiagnostic utility of the image may be improved. The characteristics ofthe grating lobe are not different from those of the rest of diagnostictissue, making general application of adaptive filtering based oncharacteristic of the ultrasound data without reference toknowledge-based detection difficult or not as effective. Usingknowledge-based detection, only the locations of the artifact aresuppressed by an amount sufficient to make the artifact less or notvisible.

In another embodiment, amplitude scaling (e.g., reduction) or greatertemporal persistence is applied to locations classified as spontaneouscontrast. Different scaling (e.g., lesser), no scaling, or differentpersistence is applied to other locations. As seen in FIG. 4, thespontaneous contrast in the left ventrical may be distracting to theuser. After amplitude scaling or temporal persistence, the spontaneouscontrast is suppressed for the left ventricle as shown in FIG. 5. Thespontaneous contrast in the other heart chamber is or is not alsosuppressed. Knowledge-based detection allows for distinguishing betweenlocations for the same artifact. In the example of FIG. 5, thesuppression is only for the spontaneous contrast in the left ventricleand not for other spontaneous contrast or the heart wall tissue. Thesuppression of the knowledge base detected artifact improves aestheticsand/or diagnostic utility of the image. Alternately, the spontaneouscontrast may be emphasized and not suppressed if so desired by the user.The spontaneous contrast may be presented with a separate color map todistinguish spontaneous contrast from the rest of the anatomy.

Other artifacts, such as shadowing, may be processed differently forimage enhancement. For example, shadowing is scaled or persisted morethan for other locations in order to reduce the shadowing by increasingthe intensities in the shadow.

In another embodiment, the ultrasound data is filtered differently bylocation based on detected anatomic structure. The detection using theknowledge base provides an overall or comprehensive structure definitionand continuity. It is often difficult for image processing algorithms todetermine whether or not two distinct pixels belong to the same anatomicstructure, especially in images with high levels of noise and clutter.If the image enhancement more aggressively connects possible pixels orvoxels by an overall setting, locations not part of the anatomy may beincluded. Using detection techniques based on an expert created databasemay allow the image processing algorithms to be much more aggressive inbuilding structure continuity since there is increased confidence inknowing which pixels to process.

Any identified artifacts or anatomy may be enhanced by any locationadaptive image processing. The location classification is used to adaptspatially the image enhancement. Specific anatomy, artifacts, and/orfeatures may be suppressed, emphasized, or altered relative to othertissue, fluid, or structure represented by the ultrasound data. After orbefore anatomy or artifact specific enhancement, further imageprocessing not specific to the detected anatomy or artifact may beapplied.

In act 22, an image is generated. The processor or ultrasound systemgenerates the image form the enhanced ultrasound data. Where theenhancement is applied to display values (e.g., RGB values), theultrasound data is presented on the display. Where the enhancement isapplied before mapping, such as after detection, scan conversion and/ormapping to display values is provided. The result is then output to adisplay.

The generated image is a B-mode, color flow mode, M-mode, pulsed waveDoppler, contrast agent, harmonic, other ultrasound image, orcombination thereof. The image represents the patient at a given time orover time. The image may represent one or more sample locations withinthe patient, such as a planar or volume region.

The image represents the patient without segmentation. Rather thanisolating information, the entire scan region is represented in theimage. One or more artifacts are suppressed and/or one or moreanatomical locations are emphasized in the image by the enhancement. Ascompared to an image generated with the ultrasound data without theknowledge-based enhancement, less artifacts may be presented to theuser, such as shown in FIGS. 3 and 5. Where both artifact suppressionand anatomy of interest emphasis are provided, the displayed ultrasoundimage provides less distracting artifacts and more focus on the anatomyof interest.

FIG. 6 shows one embodiment of a system for image enhancement in medicaldiagnostic ultrasound. Knowledge-based detection of anatomy and/orartifacts is used to enhance selectively the acquired ultrasound data.The image processing performed by the system adapts to detected anatomyand/or artifacts. The system performs the method described above forFIG. 1 or a different method.

The ultrasound system includes a transmit beamformer 52, a transducer54, a receive beamformer 56, an image processor 58, a display 60, aprocessor 62 and a memory 64. Other systems may be used. Additional,different or fewer components may be provided. For example, separatedetectors and a scan converter are also provided. As another example, auser input device (e.g., mouse and/or keyboard) is provided foraccepting user selection of an imaging application (e.g., cardiacimaging), configuration, and/or confirmation of detection. The detectormay use one or more input features from other sources than theultrasound data. Other sources of data may include sensors, a therapysystem, or other inputs. Such devices or inputs may be provided to theprocessor 62 or the memory 64. In one embodiment, all of the inputfeatures used by the detector are acquired from ultrasound data.

The system 10 is a medical diagnostic ultrasound imaging system. Imagingincludes two-dimensional, three-dimensional, B-mode, Doppler, colorflow, spectral Doppler, M-mode, strain, elasticity, harmonic, contrast,or other imaging modalities now known or later developed. The ultrasoundsystem 10 is a full size cart mounted system, a smaller portable system,a hand-held system, or other now known or later developed ultrasoundimaging system. In another embodiment, the processor 62 and memory 64are part of a separate system. For example, the processor 62 and thememory 64 are a workstation or personal computer operating independentlyof or connected with the ultrasound system. As another example, theprocessor 62 and the memory 64 are part of a therapy system.

The transducer 54 is a single, one-dimensional, multi-dimensional orother now known or later developed ultrasound transducer. Each elementof the transducer 54 is a piezoelectric, microelectromechanical,capacitive membrane ultrasound transducer, or other now known or laterdeveloped transduction element for converting between acoustic andelectrical energy. Each of the transducer elements connect to thebeamformers 52, 56 for receiving electrical energy from the transmitbeamformer 52 and providing electrical energy responsive to acousticechoes to the receive beamformer 56.

The transmit beamformer 12 is one or more waveform generators,amplifiers, delays, phase rotators, multipliers, summers,digital-to-analog converters, filters, combinations thereof, and othernow known or later developed transmit beamformer components. Thetransmit beamformer 52 is configured into a plurality of channels forgenerating transmit signals for each element of a transmit aperture. Thetransmit signals for each element are delayed and apodized relative toeach other for focusing acoustic energy along one or more scan lines.Signals of different amplitudes, frequencies, bandwidths, delays,spectral energy distributions or other characteristics are generated forone or more elements during a transmit event.

The receive beamformer 56 is configured to acquire ultrasound datarepresenting a region of a patient. The receive beamformer 56 includes aplurality of channels for separately processing signals received fromdifferent elements of the transducer 54. Each channel may includedelays, phase rotators, amplifiers, filters, multipliers, summers,analog-to-digital converters, control processors, combinations thereofand other now known or later developed receive beamformer components.The receive beamformer 56 also includes one or more summers forcombining signals from different channels into a beamformed signal. Asubsequent filter may also be provided. Other now known or laterdeveloped receive beamformers may be used. Electrical signalsrepresenting the acoustic echoes from a transmit event are passed to thechannels of the receive beamformer 56. The receiver beamformer 56outputs in-phase and quadrature, radio frequency or other datarepresenting one or more locations in a scanned region.

The receive beamformed signals are subsequently detected and used togenerate an ultrasound image by the image processor 58. The imageprocessor 58 is a B-mode/M-mode detector, Doppler/flow/tissue motionestimator, harmonic detector, contrast agent detector, spectral Dopplerestimator, combinations thereof, or other now known or later developeddevice for outputting detected ultrasound data. The detection determinesa characteristic of the acoustic response of the patient from thebeamformed data. The image processor 58 may include a scan converter,buffer for display mapping, and/or the processor 62 for imageenhancement. The detected or estimated signals, prior to or after scanconversion, may be used by the processor 62.

The processor 62 is a control processor, filter, general processor,application specific integrated circuit, field programmable gate array,digital components, analog components, hardware circuit, combinationsthereof and other now known or later developed devices for imageprocessing to enhance an image. The processor 62 is configured, withcomputer code, firmware, and/or hardware, to identify anatomy orartifacts represented in the detected data and alter the detected datato emphasize or suppress the anatomy or artifact.

The processor 62 receives, requests, and/or calculates values for theinput features to be used by knowledge-based anatomy or artifactdetection. In one embodiment, one or more of the features andcorresponding values are extracted from the ultrasound data. Theprocessor 62 performs one or more measures of data characteristics for aregion around each of various locations represented by the ultrasounddata. For example, Haar wavelet features provide one or more differentmeasures for each location represented by the ultrasound data.

The processor 62 is configured to identify an artifact and/or anatomyfrom the detected data as a function of a knowledge base. In oneembodiment, the knowledge base is represented as a machine-learntclassifier. The machine-learnt classifier is learned from the knowledgebase. Feature values are extracted and input to the classifier. Theclassifier relates the features to class membership (i.e., artifactand/or anatomy) for each location. Different classifiers may be providedfor different artifacts, anatomy, and/or applications (e.g., cardiac orradiology). In one embodiment, the classifier is a matrix trained toidentify the artifact as a grating lobe, shadow, or spontaneous contrastof blood. In alternative embodiments, other knowledge baseidentification is used, such as using matching of current ultrasounddata with one of many annotated images in a database.

The processor 62 and/or the image processor 58 are configured to imageprocess the detected data as a function of the artifact and/or anatomy.The locations associated with the detected object are handled or imageprocessed differently than other locations. Different filtering, edgeenhancement, or other image process is applied to locations of thedetected class than other locations. The difference may be in settings(i.e., apply filtering but with different characteristics), whether toprocess (i.e., enhance some locations and not others), or type ofprocessing (i.e., one type for detected anatomy or artifact and othertype for other locations). For example, the locations of a detectedartifact are filtered or scaled differently than other locations tosuppress the artifact.

The display 60 is a monitor, LCD, LED, plasma, projector, printer, orother now known or later developed display device. The processor 62and/or the image processor 58 generate display signals for the display60. The display signals, such as RGB values, may be used by theprocessor 62.

The display 60 is configured to display an image representing thescanned region of the patient, such as a B-mode image. The image isgenerated from the image processed detected data. After the adaptiveimage processing is applied, an image is generated and displayed on thedisplay 60. The image represents the scan region, but has intensities orestimated values that are altered to enhance or suppress based on thedetected locations. The image is generated from the data after the imageenhancement guided by the knowledge base detection. For example, aB-mode image with a detected artifact having been reduced is displayed.The reduction is partial or completely removed.

The memory 64 is a computer readable storage medium having storedtherein data representing instructions executable by the programmedprocessor for image enhancement in medical diagnostic ultrasound. Theinstructions for implementing the processes, methods and/or techniquesdiscussed herein are provided on computer-readable storage media ormemories, such as a cache, buffer, RAM, removable media, hard drive orother computer readable storage media. Computer readable storage mediainclude various types of volatile and nonvolatile storage media. Thefunctions, acts, or tasks illustrated in the figures or described hereinare executed in response to one or more sets of instructions stored inor on computer readable storage media. The functions, acts or tasks areindependent of the particular type of instructions set, storage media,processor or processing strategy and may be performed by software,hardware, integrated circuits, firmware, micro code and the like,operating alone or in combination. Likewise, processing strategies mayinclude multiprocessing, multitasking, parallel processing and the like.In one embodiment, the instructions are stored on a removable mediadevice for reading by local or remote systems. In other embodiments, theinstructions are stored in a remote location for transfer through acomputer network or over telephone lines. In yet other embodiments, theinstructions are stored within a given computer, CPU, GPU or system.

While the invention has been described above by reference to variousembodiments, it should be understood that many changes and modificationscan be made without departing from the scope of the invention. It istherefore intended that the foregoing detailed description be regardedas illustrative rather than limiting, and that it be understood that itis the following claims, including all equivalents, that are intended todefine the spirit and scope of this invention.

We claim:
 1. A method of image enhancement in medical diagnosticultrasound, the method comprising: acquiring, by an ultrasound system,ultrasound image data from a scan of tissue of a patient, the ultrasoundimage data representing spatial locations of the tissue; applying, by aprocessor of the ultrasound system, a machine-learnt classifier to theultrasound image data, the machine-learnt classifier having been trainedwith machine learning from a database of ground-truth annotated images,the machine-learnt classifier outputting first locations of an imagingartifact of the ultrasound system in the ultrasound image data inresponse to input of the ultrasound image data to the machine-learntclassifier; altering the ultrasound image data for the first locationsof the imaging artifact differently than for second locations, thealtering enhancing anatomy at the second locations not associated withthe imaging artifact more than the first locations of the imagingartifact; and displaying an ultrasound image of the tissue of thepatient from the altered ultrasound image data.
 2. The method of claim 1wherein acquiring comprises acquiring B-mode image data after B-modedetection.
 3. The method of claim 1 wherein applying comprises:extracting input feature values from the ultrasound image data; andoutputting the first locations in response to input of the input featurevalues to the machine-learnt classifier.
 4. The method of claim 1wherein applying comprises detecting the first locations of the imagingartifact as locations of a grating lobe, and wherein altering furthercomprises applying a filter or amplitude scaling to the first locationsof the imaging artifact and not at the second locations.
 5. The methodof claim 1 wherein applying comprises detecting the first locations ofthe imaging artifact as locations of spontaneous contrast of bloodcells, and wherein altering further comprises amplitude scaling moregreatly for or applying greater temporal persistence to the firstlocations of the imaging artifact than the second locations.
 6. Themethod of claim 1 wherein applying comprises detecting shadowing, andwherein altering further comprises reducing the shadowing with anincrease in intensity in the shadowing.
 7. The method of claim 1 whereinaltering comprises spatially adaptive filtering, the spatially adaptivefiltering adapting as a function of the first locations of the imagingartifact.
 8. The method of claim 1 further comprising: outputting anindication of the first locations of the imaging artifact; andperforming the altering after receiving user confirmation based on theoutput indication.
 9. The method of claim 1 further comprising:detecting an anatomic structure represented in the ultrasound image datawith an additional machine-learnt classifier; and filtering theultrasound image data based on the anatomic structure, whereindisplaying comprises displaying the ultrasound image from the alteredand filtered ultrasound image data.
 10. The method of claim 1 whereindisplaying comprises displaying the ultrasound image with the imagingartifact reduced relative to the ultrasound image without the altering.11. A non-transitory computer readable storage medium having storedtherein data representing instructions executable by a programmedprocessor for image enhancement in medical diagnostic ultrasound, thestorage medium comprising instructions for: receiving, from anultrasound scanner, detected ultrasound data representing a patient;classifying locations represented by the detected ultrasound data asrepresenting an artifact or anatomy, the classifying being with aknowledge base; altering the detected ultrasound data as a function ofthe classification of the locations, the altering being an altering ofamplitude of the ultrasound data for the artifact or anatomy differentlyrelative to the amplitude of the ultrasound data for representingbackground, tissue, fluid and/or another object different than theclassified artifact or anatomy while maintaining representation of allof the locations including representation of the background, tissue,fluid and/or the other object, the altering of the amplitude being byincrease in amplitude, by spatial filtering, or by persistence; andgenerating an image from the enhanced ultrasound data as altered. 12.The non-transitory computer readable storage medium of claim 11 whereinclassifying comprises classifying with the knowledge base comprisingground-truth labeled database of ultrasound data.
 13. The non-transitorycomputer readable storage medium of claim 11 wherein classifyingcomprises classifying with a machine-learnt classifier, themachine-learnt classifier learnt from the database.
 14. Thenon-transitory computer readable storage medium of claim 11 whereinclassifying comprises detecting an anatomical structure as the anatomy,and wherein altering comprises the spatial filtering as a function ofthe locations of the anatomical structure.
 15. The non-transitorycomputer readable storage medium of claim 11 wherein classifyingcomprises detecting the artifact, and wherein altering further comprisesreducing the amplitude for the locations of the artifact.
 16. Thenon-transitory computer readable storage medium of claim 11 whereinreceiving comprises receiving B-mode data, wherein altering comprisesthe spatial filtering, and wherein generating the image comprisesgenerating a B-mode image without segmentation.
 17. A system for imageenhancement in medical diagnostic ultrasound, the system comprising: areceive beamformer configured to acquire ultrasound data representing aregion of a patient; a B-mode detector, Doppler estimator, or bothconfigured to output detected data from the ultrasound data; a processorconfigured to extract input features from the detected data, identify anartifact from the detected data as a function of a knowledge base, andimage process the detected data as a function of the artifact, the imageprocess including processing the detected data for locations of theartifact differently than other locations where the other locations areenhanced more than the locations of the artifact; and a displayconfigured to display an image of the region based on the imageprocessed detected data, the image being an ultrasound image with theartifact reduced.
 18. The system of claim 17 wherein the processor isconfigured to identify with a machine-learnt classifier derived from theknowledge base.
 19. The method of claim 1 wherein enhancing comprisesemphasizing an edge or border of the anatomy with less emphasizing of anedge or border of the imaging artifact.